Gathering events, eg, going to gyms and meetings, are ubiquitous and crucial in the
spreading phenomena, which induce higher-order interactions, and thus can be described …

In recent years the research community has accumulated overwhelming evidence for the
emergence of complex and heterogeneous connectivity patterns in a wide range of …

Graph Neural Networks (graph NNs) are a promising deep learning approach for analyzing
graph-structured data. However, it is known that they do not improve (or sometimes worsen) …

Deep learning systems have been successfully applied to Euclidean data such as images,
video, and audio. In many applications, however, information and their relationships are …

In the past years, network theory has successfully characterized the interaction among the
constituents of a variety of complex systems, ranging from biological to technological, and …

From social networks such as Facebook, the World Wide Web and the Internet, to the
complex interactions between proteins in the cells of our bodies, we constantly face the …

Synchronization of an ensemble of oscillators is an emergent phenomenon present in
several complex systems, ranging from social and physical to biological and technological …

The propagations of diseases, behaviors and information in real systems are rarely
independent of each other, but they are coevolving with strong interactions. To uncover the …

Abstract Models of epidemic spreading on complex networks have attracted great attention
among researchers in physics, mathematics, and epidemiology due to their success in …

Network embedding has received increasing research attention in recent years. The existing
methods show that the high-order proximity plays a key role in capturing the underlying …